{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f0537847",
   "metadata": {},
   "source": [
    "加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4563b1cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age  height  weight gender\n",
      "0   21     163      60      M\n",
      "1   22     164      56      M\n",
      "2   21     170      50      M\n",
      "3   23     168      56      M\n",
      "4   21     169      60      M\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names = ['age', 'height', 'weight', 'gender']\n",
    "dataset = pd.read_csv(\"item6/gender-data-y.txt\", delimiter=',', names=names)\n",
    "print(dataset.head()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "516132c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    age  height  weight gender  label\n",
      "0    21   163.0    60.0      M      1\n",
      "1    22   164.0    56.0      M      1\n",
      "2    21   170.0    50.0      M      1\n",
      "3    23   168.0    56.0      M      1\n",
      "4    21   169.0    60.0      M      1\n",
      "..  ...     ...     ...    ...    ...\n",
      "95   24   192.0    73.0      F      0\n",
      "96   25   187.0    74.0      F      0\n",
      "97   20   178.0    65.0      F      0\n",
      "98   23   172.0    76.0      F      0\n",
      "99   25   173.0    78.0      F      0\n",
      "\n",
      "[100 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "#将身高何体重转换为浮点型\n",
    "dataset['height']=dataset['height'].astype(float)\n",
    "dataset['weight']=dataset['weight'].astype(float)\n",
    "\n",
    "\n",
    "\n",
    "#将性别进行数值化处理\n",
    "le =preprocessing.LabelEncoder()\n",
    "dataset['label']=le.fit_transform(dataset['gender'])\n",
    "print(dataset)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b565ea",
   "metadata": {},
   "source": [
    "数据集可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fab9de1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "data=dataset.iloc[range(0,100),range(1,3)].values\t\n",
    "target=dataset.iloc[range(0,100),range(4,5)].values.reshape(1,100)[0]\n",
    "\n",
    "#绘制散点图\n",
    "plt.scatter(data[target==0,0],data[target==0,1],s=60,c='r',marker='o') \n",
    "plt.scatter(data[target==1,0],data[target==1,1],s=60,c='g',marker='^')\n",
    "\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.xlabel('身高/cm')\n",
    "plt.ylabel('体重/kg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af640ed0",
   "metadata": {},
   "source": [
    "寻找最佳深度值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "00c15940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#划分数据集\n",
    "x,y=data,target\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=30,random_state=0)\n",
    "\n",
    "#决策树深度与模型预测误差率计算\n",
    "depth=np.arange(1,15)\n",
    "err_list=[]\n",
    "\n",
    "#开始遍历\n",
    "for i in depth:\n",
    "    model=DecisionTreeClassifier(criterion='entropy',max_depth=i)\n",
    "    model.fit(x_train,y_train)\n",
    "    pred=model.predict(x_test)\n",
    "    ac=accuracy_score(y_test,pred)\n",
    "    err=1-ac\n",
    "    err_list.append(err)\n",
    "\n",
    "#绘制决策树深度与模型预测误差率图形\n",
    "plt.plot(depth,err_list,'ro-')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.xlabel('决策树深度')\n",
    "plt.ylabel('预测误差率')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448d0301",
   "metadata": {},
   "source": [
    "开始使用5或6作为模型开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c442d04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型评估报告：\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.73      0.85        15\n",
      "           1       0.79      1.00      0.88        15\n",
      "\n",
      "    accuracy                           0.87        30\n",
      "   macro avg       0.89      0.87      0.86        30\n",
      "weighted avg       0.89      0.87      0.86        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "#决策树深度取值为5时，训练模型\n",
    "model=DecisionTreeClassifier(criterion='entropy',max_depth=5)\n",
    "model.fit(x_train,y_train)\n",
    "\n",
    "#对模型进行评估，并输出评估报告\n",
    "pred=model.predict(x_test)\n",
    "re=classification_report(y_test,pred)\n",
    "print('模型评估报告：')\n",
    "print(re)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c6ec36",
   "metadata": {},
   "source": [
    "显示分类结果，并进行数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a4a6e6f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "#绘制分类界面\n",
    "N,M=500,500\t\t               \n",
    "t1=np.linspace(140,195,N)                                      \n",
    "t2=np.linspace(30,90,M)\t\t          \n",
    "x1,x2=np.meshgrid(t1,t2)\t\t           \n",
    "x_new=np.stack((x1.flat,x2.flat),axis=1)                    \n",
    "y_predict=model.predict(x_new)\t                         \n",
    "y_hat=y_predict.reshape(x1.shape)\t          \n",
    "iris_cmap=ListedColormap([\"#ACC6C0\",\"#FF8080\"])\n",
    "plt.pcolormesh(x1,x2,y_hat,cmap=iris_cmap)\t\n",
    "\n",
    "#绘制两个类别的样本数据点\n",
    "plt.scatter(x[y==0,0],x[y==0,1],s=60,c='r',marker='o')\t\t\t                                #绘制标签为0的样本点\n",
    "plt.scatter(x[y==1,0],x[y==1,1],s=60,c='g',marker='^')\t\n",
    "\n",
    "#设置坐标轴的名称并显示图形\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.xlabel('身高/cm')\n",
    "plt.ylabel('体重/kg')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "AIG241",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
